The work of few-shot classification (FSC) is one of the most concerning issues at present. Researchers usually pay efforts from two perspectives to solve it.
On the one hand, hoping the designed feature extractor is robust enough to achieve a feature extracting process on cross-category situation.
Many classical strategies have been proposed, for examples,
[1]} and [2]} introduce knowledge distillation;
[3]} and [4]} introduce self-supervision;
[5]} and [6]} introduce manifold learning;
[7]}, [8]} introduce meta-learning.
On the other hand, strengthening the final classifier and make it ignore the influence of inaccurate feature representation to some extent, as examples,
[9]}, [10]} introduce distribution calibration;
[11]}, [12]} introduce self-training;
[13]}, [14]} introduce multi-view fusion.
Our CSN can be viewed as focusing on the first point. We introduce component-based multi-labels to make our feature extractor more adaptable for novel data. For the final classifier, we just employ one of the simplest one, shown in the following section.
